Computing Programs for Generalized Planning as Heuristic Search
This work addresses a fundamental limitation in GP by enabling heuristic search methods, which could improve efficiency and scalability for AI planning systems, though it appears incremental as it adapts existing paradigms to a new context.
The paper tackles the challenge of applying heuristic search to Generalized Planning (GP) by adapting the planning as heuristic search paradigm and introducing the first native heuristic search approach to GP, resulting in the BFGP algorithm that performs best-first search in a program-based solution space guided by evaluation and heuristic functions.
Although heuristic search is one of the most successful approaches to classical planning, this planning paradigm does not apply straightforwardly to Generalized Planning (GP). This paper adapts the planning as heuristic search paradigm to the particularities of GP, and presents the first native heuristic search approach to GP. First, the paper defines a program-based solution space for GP that is independent of the number of planning instances in a GP problem, and the size of these instances. Second, the paper defines the BFGP algorithm for GP, that implements a best-first search in our program-based solution space, and that is guided by different evaluation and heuristic functions.